Bonetti M, Gelber R D
Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, MA 02115, USA.
Stat Med. 2000 Oct 15;19(19):2595-609. doi: 10.1002/1097-0258(20001015)19:19<2595::aid-sim562>3.0.co;2-m.
We introduce the subpopulation treatment effect pattern plot (STEPP) method, designed to facilitate the interpretation of estimates of treatment effect derived from different but potentially overlapping subsets of clinical trial data. In particular, we consider sequences of subpopulations defined with respect to a covariate, and obtain confidence bands for the collection of treatment effects (here obtained from the Cox proportional hazards model) associated with the sequences. The method is aimed at determining whether the magnitude of the treatment effect changes as a function of the values of the covariate. We apply STEPP to a breast cancer clinical trial data set to evaluate the treatment effect as a function of the oestrogen receptor content of the primary tumour.
我们介绍了亚组治疗效应模式图（STEPP）方法，该方法旨在便于解释从临床试验数据的不同但可能重叠的子集中得出的治疗效应估计值。具体而言，我们考虑根据一个协变量定义的亚组序列，并获得与这些序列相关的治疗效应集合（此处从Cox比例风险模型获得）的置信带。该方法旨在确定治疗效应的大小是否随协变量值的变化而变化。我们将STEPP应用于一个乳腺癌临床试验数据集，以评估作为原发肿瘤雌激素受体含量函数的治疗效应。
